According to the feature extraction process, the supervised deep CNNs have been implemented and then removed the fully connected layer at the end of the architecture to get the hidden representations from the input images. The hidden representation is a tensor with dimensions \(\textit {H} \times \textit {W} \times \textit {C}\)  that is down-sampled from the original dimensions. Specifically, we used ResNet-34 [1]} and EfficientNet-B2 [2]} for the feature extraction step. To train ResNet-34 extractor, all input images were resized to \(\textit {H} \times \textit {W} = \textit {512} \times \textit {512}\) . Additionally, each mammogram has been duplicated from the original grayscale channel to three channels. After passing the trained extractor, we obtained the feature tensor with dimensions of \(\textit {H} \times \textit {W} \times \textit {C} = \textit {32} \times \textit {24} \times \textit {512}\) . Finally, this hidden representation was averaged-pooled over the spatial dimensions to get a 512-dimensional vector. Likewise, to train EfficientNet-B2 model, the input was \( \textit {H} \times \textit {W} \times \textit {C} = \textit {1024} \times \textit {768} \times \textit {3}\) . The hidden tensor was \(\textit {H} \times \textit {W} \times \textit {C} = \textit {32} \times \textit {24} \times \textit {1408}\)  and the hidden vector was in \(\textit {1408}\) -dimensional format.
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